Analysts in various professions may, at times, be called upon to search relatively large collections of imagery to identify, if present, various types of relevant information (referred to herein as “a target entity” or “target entities”) in the collection of imagery. For example, medical analysts sometimes diagnose a physical impairment by searching complex imagery collections to identify one or more target entities therein that may be the cause of the physical impairment. Moreover, intelligence analysts may be called upon to search relatively complex imagery collections to identify target entities therein that may relate to various types of intelligence gathering activities.
Advancements in both image collection and storage technology presently allow for the relatively low-cost storage of large volumes of high-quality imagery. However, the cost of searching through large sets of imagery for target entities can often be substantial. Indeed, in many professions, such as intelligence gathering, effective searching may rely on the expertise of highly skilled analysts, who typically search through relatively large sequences of images in a relatively slow manner. Presently, the number of skilled analysts available to search the amount of imagery that is stored, or can potentially be stored, is in many instances insufficient.
In response to the foregoing, there has relatively recently been a focus on developing various systems and methods for triaging imagery. One of the methods that has shown promise combines electroencephalography (EEG) technology and rapid serial visualization presentation (RSVP). Various implementations of this combination have been researched and developed. For example, researchers at Columbia University have experimented with a system in which users are presented, using the RSVP paradigm, a sequence of images, some of which may include particular types of target entities. During the RSVP presentation, EEG data are collected from the users. A classifier then uses the collected EEG data to assign probabilities to each image. The probabilities are representative of the likelihood an image includes a target. These assigned probabilities are then used to sort the presented images, placing those images most likely to include a target entity near the beginning of the image sequence.
Although useful in sorting a sequence of images, the above described system and method, as well as other systems and methods that employ these same technologies, do suffer certain drawbacks. For example, if the above-described image triage system and method is applied to a broad area image, it will not provide an analyst with information regarding the locations of potential targets within the context of the original broad area image. Rather, it will merely reorder the manner in which the broad area image is presented to the user. Various other systems and methods also fail to provide such location context information. Thus, once individual images from a sequence of images are identified as being most likely to include a target entity, each of those individual images will likely need to be carefully analyzed by an image analyst.
Hence, there is a need for an efficient and effective system and method for triaging individual images for target entities. Namely, a system and method that can be used to rapidly screen high volumes of imagery, including individual images, and identify a subset of images, or sections of individual images, that merit more detailed scrutiny by a skilled analyst. The present invention addresses at least this need.